Livestock production in a changing climate : adaptation and mitigation research in Australia

Climate change presents a range of challenges for animal agriculture in Australia. Livestock production will be affected by changes in temperature and water availability through impacts on pasture and forage crop quantity and quality, feed-grain production and price, and disease and pest distributions. This paper provides an overview of these impacts and the broader effects on landscape functionality, with a focus on recent research on effects of increasing temperature, changing rainfall patterns, and increased climate variability on animal health, growth, and reproduction, including through heat stress, and potential adaptation strategies. The rate of adoption of adaptation strategies by livestock producers will depend on perceptions of the uncertainty in projected climate and regional-scale impacts and associated risk. However, management changes adopted by farmers in parts of Australia during recent extended drought and associated heatwaves, trends consistent with long-term predicted climate patterns, provide some insights into the capacity for practical adaptation strategies. Animal production systems will also be significantly affected by climate change policy and national targets to address greenhouse gas emissions, since livestock are estimated to contribute ~10% of Australia’s total emissions and 8–11% of global emissions, with additional farm emissions associated with activities such as feed production. More than two-thirds of emissions are attributed to ruminant animals. This paper discusses the challenges and opportunities facing livestock industries in Australia in adapting to and mitigating climate change. It examines the research needed to better define practical options to reduce the emissions intensity of livestock products, enhance adaptation opportunities, and support the continued contribution of animal agriculture to Australia’s economy, environment, and regional communities.

Landscape ecology of the lesser grain borer, Rhyzopertha dominica

The Lesser Grain Borer is a major pest of stored grain with a global distribution. This project has, for the first time recorded this pest throughout broad spatial areas, tens of kilometres from grain production or storage. Statistical analysis revealed that different factors such as ambient temperature and the availability of food resources affect R. dominica differently between different habitats. This suggests that, contrary to the prevailing view, this pest is not solely dependent on stored wheat and can continue to persist throughout a range of habitats. These findings have important management implications for Australia's wheat industry.

A Bayesian analysis of an agricultural field trial with three spatial dimensions

Modern technology now has the ability to generate large datasets over space and time. Such data typically exhibit high autocorrelations over all dimensions. The field trial data motivating the methods of this paper were collected to examine the behaviour of traditional cropping and to determine a cropping system which could maximise water use for grain production while minimising leakage below the crop root zone. They consist of moisture measurements made at 15 depths across 3 rows and 18 columns, in the lattice framework of an agricultural field. Bayesian conditional autoregressive (CAR) models are used to account for local site correlations. Conditional autoregressive models have not been widely used in analyses of agricultural data. This paper serves to illustrate the usefulness of these models in this field, along with the ease of implementation in WinBUGS, a freely available software package. The innovation is the fitting of separate conditional autoregressive models for each depth layer, the ‘layered CAR model’, while simultaneously estimating depth profile functions for each site treatment. Modelling interest also lay in how best to model the treatment effect depth profiles, and in the choice of neighbourhood structure for the spatial autocorrelation model. The favoured model fitted the treatment effects as splines over depth, and treated depth, the basis for the regression model, as measured with error, while fitting CAR neighbourhood models by depth layer. It is hierarchical, with separate onditional autoregressive spatial variance components at each depth, and the fixed terms which involve an errors-in-measurement model treat depth errors as interval-censored measurement error. The Bayesian framework permits transparent specification and easy comparison of the various complex models compared.

Increasing Food Production Sustainably in a Changing Climate : Understanding the Pressures and Potential

Australian agriculture is faced with the dilemma of increasing food production for a growing domestic and world population while decreasing environmental impacts and supporting the social and economic future of regional communities. The challenge for farmers is compounded by declining rates of productivity growth which have been linked to changes in climate and decreasing investment in agricultural research. The answer must lie in understanding the ecological functionality of landscapes and matching management of agricultural systems and use of natural resources to landscape capacity in a changing climate. A simplified mixed grain and livestock farm case study is used to illustrate the challenges of assessing the potential for shifts in land allocation between commodities to achieve sustainable intensification of nutrition production. This study highlights the risks associated with overly-simplistic solutions and the need for increased investment in research to inform the development of practical strategies for increasing food production in Australian agro-ecosystems while managing the impacts of climate change and addressing climate change mitigation policies.

Production of fine-grained particles

A method of producing particles having nano-sized grains comprises the steps of: (a) prepg. a soln. contg. one or more metal cations; (b) mixing the soln. from step (a) with one or more surfactants to form a surfactant/liq. mixt. and (c) heating the mixt. from step (b) above to form the particles. [on SciFinder(R)]

Method for the production of porous complex metal oxides using a porogen

A method of producing porous complex oxides includes the steps of providing a mixt. of (a) precursor elements suitable to produce the complex oxide, or (b) one or more precursor elements suitable to produce particles of the complex oxide and one or more metal oxide particles; and (c) a particulate carbon-contg. pore-forming material selected to provide pore sizes in the range of 7-250 nm, and treating the mixt. to (i) form the porous complex oxide in which two or more of the precursor elements from (a) above or one or more of the precursor elements and one or more of the metals in the metal oxide particles from (b) above are incorporated into a phase of the complex metal oxide and the complex metal oxide has grain sizes in the range of 1-150 nm, and (ii) removing the pore-forming material under conditions such that the porous structure and compn. of the complex oxide is substantially preserved. The method may be used to produce nonrefractory metal oxides as well. The mixt. further includes a surfactant, or a polymer. [on SciFinder(R)]

Production of fine-grained particles

A method of producing particles having nano-sized grains comprises the steps of: (a) prepg. a soln. contg. one or more metal cations; (b) mixing the soln. from step (a) with one or more surfactants to form a surfactant/liq. mixt. and (c) heating the mixt. from step (b) above to form the particles. [on SciFinder(R)]

Optimal Irrigation and N-fertilizer Management for Sustainable Winter Wheat Production in Khorezm, Uzbekistan

The efficiency of the nitrogen (N) application rates 0, 120, 180 and 240 kg N ha−1 in combination with low or medium water levels in the cultivation of winter wheat (Triticum aestivum L.) cv. Kupava was studied for the 2005–2006 and 2006–2007 growing seasons in the Khorezm region of Uzbekistan. The results show an impact of the initial soil Nmin (NO3-N + NH4-N) levels measured at wheat seeding on the N fertilizer rates applied. When the Nmin content in the 0–50 cm soil layer was lower than 10 mg kg−1 during wheat seeding in 2005, the N rate of 180 kg ha−1 was found to be the most effective for achieving high grain yields of high quality. With a higher Nmin content of about 30 mg kg−1 as was the case in the 2006 season, 120 kg N ha−1 was determined as being the technical and economical optimum. The temporal course of N2O emissions of winter wheat cultivation for the two water-level studies shows that emissions were strongly influenced by irrigation and N-fertilization. Extremely high emissions were measured immediately after fertilizer application events that were combined with irrigation events. Given the high impact of N-fertilizer and irrigation-water management on N2O emissions, it can be concluded that present N-management practices should be modified to mitigate emissions of N2O and to achieve higher fertilizer use efficiency.